libotb-dev 6.4.0+dfsg-1 / usr / include / OTB-6.4 / otbTrainGradientBoostedTree.txx

/*
 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#ifndef otbTrainGradientBoostedTree_txx
#define otbTrainGradientBoostedTree_txx
#include "otbLearningApplicationBase.h"
#include "otbGradientBoostedTreeMachineLearningModel.h"

namespace otb
{
namespace Wrapper
{

template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitGradientBoostedTreeParams()
{
// disable GBTree model with OpenCV 3 (not implemented)
#ifndef OTB_OPENCV_3
  AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
  SetParameterDescription(
      "classifier.gbt",
      "This group of parameters allows setting Gradient Boosted Tree classifier parameters. "
      "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");

  if (m_RegressionFlag)
    {
    AddParameter(ParameterType_Choice, "classifier.gbt.t", "Loss Function Type");
    SetParameterDescription("classifier.gbt.t","Type of loss functionused for training.");
    AddChoice("classifier.gbt.t.sqr","Squared Loss");
    AddChoice("classifier.gbt.t.abs","Absolute Loss");
    AddChoice("classifier.gbt.t.hub","Huber Loss");
    }

  //WeakCount
  AddParameter(ParameterType_Int, "classifier.gbt.w", "Number of boosting algorithm iterations");
  SetParameterInt("classifier.gbt.w",200, false);
  SetParameterDescription(
      "classifier.gbt.w",
      "Number \"w\" of boosting algorithm iterations, with w*K being the total number of trees in "
      "the GBT model, where K is the output number of classes.");

  //Shrinkage
  AddParameter(ParameterType_Float, "classifier.gbt.s", "Regularization parameter");
  SetParameterFloat("classifier.gbt.s",0.01, false);
  SetParameterDescription("classifier.gbt.s", "Regularization parameter.");

  //SubSamplePortion
  AddParameter(ParameterType_Float, "classifier.gbt.p",
               "Portion of the whole training set used for each algorithm iteration");
  SetParameterFloat("classifier.gbt.p",0.8, false);
  SetParameterDescription(
      "classifier.gbt.p",
      "Portion of the whole training set used for each algorithm iteration. The subset is generated randomly.");

  //MaxDepth
  AddParameter(ParameterType_Int, "classifier.gbt.max", "Maximum depth of the tree");
  SetParameterInt("classifier.gbt.max",3, false);
  SetParameterDescription(
        "classifier.gbt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
        "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "
        "if the tree is pruned.");

  //UseSurrogates : don't need to be exposed !
  //AddParameter(ParameterType_Empty, "classifier.gbt.sur", "Surrogate splits will be built");
  //SetParameterDescription("classifier.gbt.sur","These splits allow working with missing data and compute variable importance correctly.");
#endif
}

template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainGradientBoostedTree(typename ListSampleType::Pointer trainingListSample,
                           typename TargetListSampleType::Pointer trainingLabeledListSample,
                           std::string modelPath)
{
#ifdef OTB_OPENCV_3
  (void) trainingListSample;
  (void) trainingLabeledListSample;
  (void) modelPath;
#else
  typedef otb::GradientBoostedTreeMachineLearningModel<InputValueType, OutputValueType> GradientBoostedTreeType;
  typename GradientBoostedTreeType::Pointer classifier = GradientBoostedTreeType::New();
  classifier->SetRegressionMode(this->m_RegressionFlag);
  classifier->SetInputListSample(trainingListSample);
  classifier->SetTargetListSample(trainingLabeledListSample);
  classifier->SetWeakCount(GetParameterInt("classifier.gbt.w"));
  classifier->SetShrinkage(GetParameterFloat("classifier.gbt.s"));
  classifier->SetSubSamplePortion(GetParameterFloat("classifier.gbt.p"));
  classifier->SetMaxDepth(GetParameterInt("classifier.gbt.max"));

  if (m_RegressionFlag)
    {
    switch (GetParameterInt("classifier.gbt.t"))
      {
      case 0: // SQUARED_LOSS
        classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
        break;
      case 1: // ABSOLUTE_LOSS
        classifier->SetLossFunctionType(CvGBTrees::ABSOLUTE_LOSS);
        break;
      case 2: // HUBER_LOSS
        classifier->SetLossFunctionType(CvGBTrees::HUBER_LOSS);
        break;
      default:
        classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
        break;
      }
    }
  else
    {
    classifier->SetLossFunctionType(CvGBTrees::DEVIANCE_LOSS);
    }

  classifier->Train();
  classifier->Save(modelPath);
#endif
}

} //end namespace wrapper
} //end namespace otb

#endif